Complementarity-based selection strategy for genomic selection

被引:10
|
作者
Moeinizade, Saba [1 ]
Wellner, Megan [1 ]
Hu, Guiping [1 ]
Wang, Lizhi [1 ]
机构
[1] Iowa State Univ, Ind & Mfg Syst Engn Dept, Ames, IA 50011 USA
基金
美国国家科学基金会;
关键词
SEXUAL SELECTION; FOOD SECURITY;
D O I
10.1002/csc2.20070
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Genomic selection is a technique that breeders use to select plant or animal individuals to mate and produce new generations of species. The conventional selection method is to select individuals that are either observed or predicted to be the best based on the assumption that parents with better phenotypes will produce better offspring. A major limitation of this method is its focus on the short-term genetic gains at the cost of genetic diversity and long-term growth potential. Recently, several new genomic selection methods were proposed to maximize the long-term potential. Along this research direction, we propose a new method, the complementarity-based selection strategy (CBS), to improve the tradeoff between short-term genetic gain and long-term potential. This approach is inspired by the genetic compatibility mate-choice mechanism in animals. Our selection method selects the individual with the highest genomic estimated breeding value to emphasize short-term achievement and then pairs it with the individual that is the most complementary to the one with highest genomic estimated breeding value to emphasize the long-term growth potential. The CBS method allows favorable alleles to be accounted for within the selection and more of them to be included. We present simulation results that compare the performance of the new method against the state-of-the-art methods in the literature and show that the CBS approach has a great potential to further improve long-term response in genomic selection.
引用
收藏
页码:149 / 156
页数:8
相关论文
共 50 条
  • [1] Improving Neuroevolution with Complementarity-Based Selection Operators
    Tomás H. Maul
    Neural Processing Letters, 2016, 44 : 887 - 911
  • [2] DISCRIMINATION STRUCTURE COMPLEMENTARITY-BASED FEATURE SELECTION
    Wang, Shuqin
    Wei, Jinmao
    Yang, Zhenglu
    COMPUTATIONAL INTELLIGENCE, 2017, 33 (04) : 863 - 898
  • [3] Improving Neuroevolution with Complementarity-Based Selection Operators
    Maul, Tomas H.
    NEURAL PROCESSING LETTERS, 2016, 44 (03) : 887 - 911
  • [4] ANNOTATION PIPELINES IMPACT OUTCOME OF METABOLIC COMPLEMENTARITY-BASED COMMUNITY SELECTION OF ECTOCARPUS (BROWN ALGAE) - ASSOCIATED BACTERIA
    Karimi, Elham
    Geslain, Enora
    Belcour, Arnaud
    Frioux, Clemence
    Aite, Meziane
    Siegel, Anne
    Corre, Erwan
    Dittami, Simon M.
    PHYCOLOGIA, 2021, 60 : 47 - 48
  • [5] Improving Response in Genomic Selection with a Population-Based Selection Strategy: Optimal Population Value Selection
    Goiffon, Matthew
    Kusmec, Aaron
    Wang, Lizhi
    Hu, Guiping
    Schnable, Patrick S.
    GENETICS, 2017, 206 (03) : 1675 - 1682
  • [6] Complementarity-based models for financial transmission rights
    Bautista, G
    Quintana, VH
    2005 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS, 1-3, 2005, : 440 - 446
  • [7] Complementarity-based Dynamic Simulation for Kinodynamic Motion Planning
    Chakraborty, Nilanjan
    Akella, Srinivas
    Trinke, Jeff
    2009 IEEE-RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, 2009, : 787 - 794
  • [8] A complementarity-based rolling friction model for rigid contacts
    Tasora, Alessandro
    Anitescu, Mihai
    MECCANICA, 2013, 48 (07) : 1643 - 1659
  • [9] An adaptive heuristic for feature selection based on complementarity
    Sumanta Singha
    Prakash P. Shenoy
    Machine Learning, 2018, 107 : 2027 - 2071
  • [10] An adaptive heuristic for feature selection based on complementarity
    Singha, Sumanta
    Shenoy, Prakash P.
    MACHINE LEARNING, 2018, 107 (12) : 2027 - 2071